Significant increases in the use of sophisticated systems have enabled world-wide collaboration through home PCs and, more recently, through ubiquitous hand-held devices. The purposes are as numerous as they are varied, and may include content sharing, whether through blogs or many well-known peer-to-peer (P2P) applications. For example, collaborative computation, starting from the early SETI@home project (setiathome.berkeley.edu) was one of the early large-scale grid computing instances.
People are gaining awareness of the power of collaborating through the network, including, for example, social and/or political collaborations. For example, recent instances have occurred where people organized themselves using digital platforms. As the crowd may become more aware of its power, a next natural step may be to enhance the tools and modalities for collaborative computing. Powerful devices, like smartphones and tablets, are able to carry out an impressive amount and array of computation. P2P computing has been shown to be feasible and efficient. For example, services such as Skype, have shown that the model may be valid and may challenge serious cloud-based competitors, such as Google Voice.
By virtue of machines being connected, people may also be “connected” to the network combining their computing and thinking capacity. Trends may be indicating that this model may gain the ability to complement and/or substitute cloud computing by connecting people and machines in a single network. Currently, many people are asynchronously analyzing, synthesizing, providing opinion and labeling and transcribing data that can be automatically mined, indexed and even learned. In this regard, there may be little effective difference between crowdsourcing and classical computing in that the “crowd” is working online, taking digital data as input, and yielding digital data as output. The main difference is that human brain-guided computation is able to perform tasks that computers can hardly do, at overwhelming speeds. Tagging a picture or a video based on their content or answering questions in natural language, are just a couple of examples.
The term crowdsourcing may refer to the increasing practice of outsourcing tasks to a network as an open call over a variety of users. Crowdsourcing may have evolved to exploit the work potential of a large crowd of people remotely connected through a network. For instance, recent efforts have studied different typologies and uses of crowdsourcing and have proposed a possible taxonomy. The suggested taxonomy has categorized crowdsourcing depending on different methodologies and processes divided according to several dimensions that are shown to impact on the behavior of workers within the crowd, and the tasks that can be outsourced to the crowd.
The term thing-sourcing may refer to the idea of mixing the concepts of crowd-sourcing and the Internet of Things (IoT) to provide collaborative solutions from humans and machines working together to solve problems. Thing-sourcing may also bring the concept of “open calls”, which may be typical from crowd-sourcing to the IoT domain.
Crowd-sourcing can be used to perform a wide range of tasks. Typically, tasks may be split into several sub-tasks, each of which performed by a single person. How these sub-tasks are organized (i.e., collaboration workflow) may have a significant impact on the performance, cost, time, and/or quality of the output. Thus, determining how to create collaborative workflows may be a relevant problem for crowd-sourcing. Typical solutions may rely on humans making decisions on how to organize a collaborative workflow.
Similar challenges may arise for thing-sourcing. However, since many of the actors in thing-sourcing are machines and not humans, different factors may be considered. For example, machines may be significantly less flexible than humans in the types of tasks they can perform and may likely have less self-awareness. As such, it may be advantageous to determine if there is a suitable workflow that may be used to complete a particular task.